Biology subjects

Randhawa, G. S.

Publications and source records attributed to Randhawa, G. S..

6 recordsLinked to original sources

bioRxiv2025

Genome-wide Pervasiveness and Localized Variation of k-mer-based Genomic Signatures in Eukaryotes

Genomic signatures are taxon-specific patterns in nucleotide sequence composition observed across different regions of a genome, used in the taxonomic classification of organisms and in inferring their evolutionary relationships. However, the nature and extent of the pervasiveness of a genomic signature across the expanse of a Telomere-to-Telomere (T2T) assembly, especially across the functionally diverse sequence elements and highly repetitive regions, remain counterintuitive and underexplored. This study aims to bridge this knowledge gap by systematically investigating the pervasiveness and variation of the genomic signature across the human genome and the genome of each of three other eukaryotic species from different kingdoms. Using the alignment-free k-mer-based Frequency Chaos Game Representation (FCGR) of DNA sequences, this study qualitatively and quantitatively analyzes the variations of the genomic signature along an entire genome. Qualitative analysis is first performed through visual inspection of FCGR patterns across different chromosomes of a species. In parallel, a quantitative analysis evaluates the variation of the genomic signature within a genome by comparing eight distance measures to identify the optimal one for the datasets in this study. By taking an intragenomic perspective with detailed analysis of chromosome landscapes these analyses reveal that, while the genomic signature is preserved in most genomic regions, exceptions exist in localized regions, such as tandem repetitions of short and long repeat units. Upon determining this pervasiveness, we assemble novel pipelines aimed at selecting a short contiguous representative genomic segment that encapsulates the sequence composition patterns characteristic of the entire genome. These representative segments are then used to assess intragenomic variation of the genomic signature, demonstrating that only a small proportion of segments (namely those characterized by regional density of short and long tandem repeats) show high distance values from the representative. No-tably, in the human genome, 80% of the segments have a distance of less than 0.24 (on a [0,1] DSSIM scale) from the representative. Moreover, we demonstrate that using these representative segments improves down-stream tasks, e.g., increasing one-nearest-neighbor (1-NN) taxonomic classification accuracy by 7% compared to selecting a random genomic segment to serve as a proxy of the genome. Lastly, this study presents a special-purpose graphical user interface (GUI) software tool, CGR-Diff, designed to provide both visual and quantitative comparisons of FCGRs of sample or user-provided DNA sequences, thereby facilitating intragenomic variation analysis of genomic signature within and across species.

bioinformatics↗

bioRxiv2025

Life at the extremes: Maximally divergent microbes with similar genomic signatures linked to extreme environments

Extreme environments impose strong mutation and selection pressures that drive distinctive, yet understudied, genomic adaptations in extremophiles. In this study, we identify 15 bacterium-archaeon pairs that exhibit highly similar k-mer-based genomic signatures despite maximal taxonomic divergence, suggesting that shared environmental conditions can produce convergent, genome-wide patterns that transcend evolutionary distance. To uncover these patterns, we developed a computational pipeline to select a composite genome proxy assembled from non-contiguous subsequences of the genome. Using supervised machine learning on a curated dataset of 693 extremophile microbial genomes, we found that 6-mers and 100 kbp genome proxy lengths provide the best balance between classification accuracy and computational efficiency. Our results provide conclusive evidence of the pervasive nature of k-mer-based patterns across the genome, and uncover the presence of taxonomic and environmental components that persist across all regions of the genome. The 15 bacterium-archaeon pairs identified by our method as having similar genomic signatures were validated through multiple independent analyses, including 3-mer frequency profile comparisons, phenotypic trait similarity, and geographic co-occurrence data. These complementary validations confirmed that extreme environmental pressures can override traditionally recognized taxonomic components at the whole-genome level. Together, these findings reveal that adaptation to extreme conditions can carry robust, taxonomic domain-spanning imprints on microbial genomes, offering new insight into the relationship between environmental mutagenesis and selection and genome-wide evolutionary convergence.

bioinformatics↗

bioRxiv2023

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of [~]700 extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, 1 [≤]k [≤]6. The supervised learning resulted in high accuracies for taxonomic classifications at 2 [≤]k [≤]6, and medium to medium-high accuracies for environment category classifications of the same datasets at 3 [≤]k [≤]6. For k = 3, our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life.

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bioRxiv2021

SomaticSiMu: a mutational signature simulator

SummarySomaticSiMu is an in silico simulator of single and double base substitutions, and single base insertions and deletions in an input genomic sequence to mimic mutational signatures. SomaticSiMu outputs simulated DNA sequences and mutational catalogues with imposed mutational signatures. The tool is the first mutational signature simulator featuring a graphical user interface, control of mutation rates, and built-in visualization tools of the simulated mutations. Simulated datasets are useful as a ground truth to test the accuracy and sensitivity of DNA sequence classification tools and mutational signature extraction tools under different experimental scenarios. The reliability of SomaticSiMu was affirmed by 1) supervised machine learning classification of simulated sequences with different mutation types and burdens, and 2) mutational signature extraction from simulated mutational catalogs. Availability and ImplementationSomaticSiMu is written in Python 3.8.3. The open-source code, documentation, and tutorials are available at https://github.com/HillLab/SomaticSiMu under the terms of the Creative Commons Attribution 4.0 International License. Contactkhill22@uwo.ca Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses

As of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearmans rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

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bioRxiv2019

MLDSP-GUI: An alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

SummaryMLDSP-GUI (Machine Learning with Digital Signal Processing) is an open-source, alignment-free, ultrafast, computationally lightweight, standalone software tool with an interactive Graphical User Interface (GUI) for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others.\n\nAvailabilityMLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/.\n\nContactgrandha8@uwo.ca\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗